The use of observed wearable sensor data (e.g., photoplethysmograms [PPG]) to infer health measures (e.g., glucose level or blood pressure) is a very active area of research. Such technology can have a significant impact on health screening, chronic disease management and remote monitoring. A common approach is to collect sensor data and corresponding labels from a clinical grade device (e.g., blood pressure cuff), and train deep learning models to map one to the other. Although well intentioned, this approach often ignores a principled analysis of whether the input sensor data has enough information to predict the desired metric. We analyze the task of predicting blood pressure from PPG pulse wave analysis. Our review of the prior work reveals that many papers fall prey data leakage, and unrealistic constraints on the task and the preprocessing steps. We propose a set of tools to help determine if the input signal in question (e.g., PPG) is indeed a good predictor of the desired label (e.g., blood pressure). Using our proposed tools, we have found that blood pressure prediction using PPG has a high multi-valued mapping factor of 33.2% and low mutual information of 9.8%. In comparison, heart rate prediction using PPG, a well-established task, has a very low multi-valued mapping factor of 0.75% and high mutual information of 87.7%. We argue that these results provide a more realistic representation of the current progress towards to goal of wearable blood pressure measurement via PPG pulse wave analysis.

翻译：利用可穿戴传感器数据（如光电容积脉搏波[PPG]）推断健康指标（如血糖水平或血压）是当前非常活跃的研究领域。此类技术对健康筛查、慢性病管理和远程监测具有重大影响。常见方法是收集来自临床级设备（如血压袖带）的传感器数据及对应标签，并训练深度学习模型实现两者间的映射。尽管初衷良好，但该方法常忽略对输入传感器数据是否包含足够信息以预测目标指标的原则性分析。我们分析了通过PPG脉搏波分析预测血压的任务。对先前研究的回顾表明，许多论文存在数据泄露、任务约束及预处理步骤不切实际的问题。我们提出一套工具，用于判断输入信号（如PPG）是否确实是预测目标标签（如血压）的良好指标。使用所提工具，我们发现基于PPG的血压预测具有33.2%的高多值映射因子和9.8%的低互信息量。相比之下，基于PPG的心率预测（一项成熟任务）仅有0.75%的低多值映射因子和87.7%的高互信息量。我们认为，这些结果更真实地反映了当前通过PPG脉搏波分析实现可穿戴血压测量的研究进展。